code: def rai_forecasting_insights_for_served_model():
      (
          X_train,
          X_test,
          y_train,
          y_test,
      ) = create_tiny_forecasting_dataset()
      train = X_train.copy()
      train["target"] = y_train
      test = X_test.copy()
      test["target"] = y_test
      model = RandomForecastingModel()
  
      # create RAI Insights and save it
      rai_insights = RAIInsights(
          model=model,
          train=train,
          test=test,
          target_column="target",
          task_type="forecasting",
          feature_metadata=FeatureMetadata(
              datetime_features=["time"], time_series_id_features=["id"]
          ),
          forecasting_enabled=True,
      )
      rai_insights.save(RAI_INSIGHTS_DIR_NAME)
  
file_path: responsible-ai-toolbox/responsibleai/tests/rai_insights/test_served_model.py
imports: import json
  import random
  from unittest import mock
  import pytest
  import requests
  from tests.common_utils import RandomForecastingModel, create_tiny_forecasting_dataset
  from responsibleai import FeatureMetadata, RAIInsights
label_desc: Use image node_id 1 for calling a global function with example usage: rai_forecasting_insights_for_served_model() without return types